Overview

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‍Research Internships at Microsoft provide a dynamic environment for research careers with a network of world-class research labs led by globally-recognized scientists and engineers, who pursue innovation in a range of scientific and technical disciplines to help solve complex challenges in diverse fields, including computing, healthcare, economics, and the environment.

Microsoft Health Futures is an interdisciplinary team of scientists, engineers, and medical professionals working at the forefront of academic research, enterprise software development, and real-world healthcare delivery. Our team combines rigorous research with rapid software iterations and impactful deployments, all within a dynamic, startup-like environment.

The human immune system is an extraordinary diagnostic network, continually adapting to detect and fight disease. This adaptability results in widely varied immune responses, shaped by factors like disease complexity (e.g., infectious and auto-immune diseases and cancer) and genetics. Given this variability and the immune system’s central role in health, developing sophisticated models of immune function is essential to advancing precision medicine.

The Immunomics group within Health Futures is dedicated to this vision. We build large-scale models of immune responses and immune cells, integrating statistical modeling and machine learning (ML) techniques such as representation learning, survival analysis, causal inference and generative modeling, alongside foundational immunological research. Our approach combines statistical modeling and advanced ML techniques with deep biological insights into areas like cancer biology, antigen presentation, an aging immune system and immune cell behavior. We believe that both cutting-edge machine learning and domain expertise are crucial to driving meaningful progress.

We invite applications for a Research Internship focused on unraveling the complexities of the human immune system. For full consideration, please submit your materials by January 19, 2026. Note: Reference letters are not required, despite any language in the qualifications section.

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‍Responsibilities‍

Research Interns put inquiry and theory into practice. Alongside fellow doctoral candidates and some of the world’s best researchers, Research Interns learn, collaborate, and network for life. Research Interns not only advance their own careers, but they also contribute to exciting research and development strides. During the 12-week internship, Research Interns are paired with mentors and expected to collaborate with other Research Interns and researchers, present findings, and contribute to the vibrant life of the community. Research internships are available in all areas of research, and are offered year-round, though they typically begin in the summer.

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‍Qualifications‍

Required Qualifications

• Accepted or currently enrolled in a PhD program in Machine Learning, Statistics, Computer Science, Computational Biology or other related field.

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Other Requirements

• Research Interns are expected to be physically located in their manager’s Microsoft worksite location for the duration of their internship.
• In addition to the qualifications below, you’ll need to submit a minimum of two reference letters for this position as well as a cover letter and any relevant work or research samples. After you submit your application, a request for letters may be sent to your list of references on your behalf. Note that reference letters cannot be requested until after you have submitted your application, and furthermore, that they might not be automatically requested for all candidates. You may wish to alert your letter writers in advance, so they will be ready to submit your letter.

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Preferred Qualifications

• Ability to develop original research agendas demonstrated by a publication record as a lead author.
• Skills analyzing large datasets including but not limited to large-scale learning, experimental design, and statistical modeling.
• Able to collaborate and communicate across disciplines as part of a cross-functional team.
• Experience working with complex biological datasets.

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